Standard network meta‐analysis and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any factors that interact with treatment effects (effect modifiers) are balanced across populations. Population adjustment methods such as multilevel network meta‐regression (ML‐NMR), matching‐adjusted indirect comparison (MAIC), and simulated treatment comparison (STC) relax this assumption using individual patient data from one or more studies, and are becoming increasingly prevalent in health technology appraisals and the applied literature. Motivated by an applied example and two recent reviews of applications, we undertook an extensive simulation study to assess the performance of these methods in a range of scenarios under various failures of assumptions. We investigated the impact of varying sample size, missing effect modifiers, strength of effect modification and validity of the shared effect modifier assumption, validity of extrapolation and varying between‐study overlap, and different covariate distributions and correlations. ML‐NMR and STC performed similarly, eliminating bias when the requisite assumptions were met. Serious concerns are raised for MAIC, which performed poorly in nearly all simulation scenarios and may even increase bias compared with standard indirect comparisons. All methods incur bias when an effect modifier is missing, highlighting the necessity of careful selection of potential effect modifiers prior to analysis. When all effect modifiers are included, ML‐NMR and STC are robust techniques for population adjustment. ML‐NMR offers additional advantages over MAIC and STC, including extending to larger treatment networks and producing estimates in any target population, making this an attractive choice in a variety of scenarios.

中文翻译：

---

评估锚定间接比较的人口调整方法的性能：模拟研究


标准网络荟萃分析和间接比较结合了有关感兴趣治疗的多项研究的汇总数据，假设与治疗效果（效果调节剂）相互作用的任何因素在人群之间是平衡的。诸如多级网络元回归（ML-NMR）、匹配调整间接比较（MAIC）和模拟治疗比较（STC）等群体调整方法使用来自一项或多项研究的个体患者数据放松了这一假设，并且变得越来越普遍卫生技术评估和应用文献。在一个应用示例和最近两次应用审查的推动下，我们进行了广泛的模拟研究，以评估这些方法在各种假设失败的情况下的性能。我们研究了不同样本量、缺失效应修正因子、效应修正强度和共享效应修正因子假设的有效性、外推法的有效性和不同研究间重叠以及不同协变量分布和相关性的影响。 ML-NMR 和 STC 的表现类似，在满足必要的假设时消除了偏差。人们对 MAIC 提出了严重的担忧，它在几乎所有模拟场景中都表现不佳，与标准间接比较相比甚至可能增加偏差。当缺少效果调节剂时，所有方法都会产生偏差，这凸显了在分析之前仔细选择潜在效果调节剂的必要性。当包含所有效应调节剂时，ML-NMR 和 STC 是用于群体调整的稳健技术。 与 MAIC 和 STC 相比，ML-NMR 具有更多优势，包括扩展到更大的治疗网络并在任何目标人群中进行估计，这使其成为各种情况下有吸引力的选择。